Missouri University of Science and Technology is an institution of higher learning located in Rolla, Missouri, United States, and part of the University of Missouri System. Most of its 8,642 students study engineering, computing, mathematics and the science. Although known primarily as an engineering school, Missouri S&T has numerous majors in humanities, social science, arts, science and business.The school is known for its repeated success in national engineering design competitions and its century-long tradition of aggrandized celebrations surrounding Saint Patrick's Day. Wikipedia.
Agency: NSF | Branch: Standard Grant | Program: | Phase: CM - Cybermanufacturing System | Award Amount: 505.29K | Year: 2017
Smart manufacturing integrates information, technology, and human ingenuity to inspire the next revolution in the manufacturing industry. Manufacturing has been identified as a key strategic investment area by the U.S. government, private sector, and university leaders to spur innovation and keep America competitive. However, the lack of new methodologies and tools is challenging continuous innovation in the smart manufacturing industry. This award supports fundamental research to develop a cyber-physical sensing, modeling, and control infrastructure, coupled with augmented reality, to significantly improve the efficiency of future workforce training, performance of operations management, safety and comfort of workers for smart manufacturing. Results from this research are expected to transform the practice of worker-machine-task coordination and provide a powerful tool for operations management. This research involves several disciplines including sensing, data analytics, modeling, control, augmented reality, and workforce training and will provide unique interdisciplinary training opportunities for students and future manufacturing engineers.
An effective way for manufacturers to tackle and outpace the increasing complexity of product designs and ever-shortening product lifecycles is to effectively develop and assist the workforce. Yet the current management of manufacturing workforce systems relies mostly on the traditional methods of data collection and modeling, such as subjective observations and after-the-fact statistics of workforce performance, which has reached a bottleneck in effectiveness. The goal of this project is to investigate an integrated set of cyber-physical system methods and tools to sense, understand, characterize, model, and optimize the learning and operation of manufacturing workers, so as to achieve significantly improved efficiency in worker training, effectiveness of behavioral operations management, and safety of front-line workers. The research team will instrument a suite of sensors to gather real-time data about individual workers, worker-machine interactions, and the working environment,develop advanced methods and tools to track and understand workers actions and physiological status, and detect their knowledge and skill deficiencies or assistance needs in real time. The project will also establish mathematical models that encode the manufacturing process in the research sensing and analysis framework, characterize the efficiency of worker-machine-task coordination, model the learning curves of individual workers, investigate various multi-modal augmented reality-based visualization, guidance, control, and intervention schemes to improve task efficiency and worker safety, and deploy, test, and conduct comprehensive performance assessments of the Researched technologies.
Agency: NSF | Branch: Standard Grant | Program: | Phase: CDS&E | Award Amount: 350.00K | Year: 2016
PI: Barua, Dipak
Proposal Number: 1609642
This multidisciplinary proposal aims to model multicellular biological systems based on modeling of the behavior of each individual cell. This research will relate sub-cellular protein interactions to full scale cell behavior, and to the behavior of a system of many cells using computational methods. Cell division, growth, death and function will be modeled. Results from this work can have an impact both on Bioengineering and on Computer Science.
Traditional computational modeling and simulation methods are inadequate to address the biological complexity of cells. In recent years, considerable efforts have been devoted to developing multiscale models that capture biological complexity at distinct time and spatial resolutions. However, little progress has been made in developing multiscale models capable of integrating high resolution biological details with long-time cellular behavior. The primary goal of this proposal is to develop a parallel computation framework, called ParCell, for multiscale cell population modeling and simulation. ParCell will link subcellular biochemistry to long-time cell behavior determined by cell death, division, fate decision, and other cellular functions. Current multiscale population models mostly rely on serial computation-based simulation techniques. Such limitations and challenges prohibit the understanding and analysis of many important biological systems, such as tissue regeneration, clonal expansion of antigen-exposed immune cells, cell migration in wound healing, evolution of drug resistance in cells, and cellular phenotypes under disease and treatment conditions. ParCell will be the framework for modeling of heterogeneous multicellular systems that will link high resolution molecular details of signaling and gene transcription to evolutionary cell fate decisions and population dynamics. Current cell population models are mostly based on agent-based modeling (ABM) technique, where cells are represented as software objects or agents. Instead, it is proposed that cells will be represented as stand-alone parallel simulations (i.e., threads) rather than software objects. Using parallel computation, ParCell will systematically expand a single-cell biochemical network model, created using other software or languages, into a population model. Specifically, it will launch parallel simulations on a single-cell biochemical network model, and treat each simulation thread as an independent cell. It will also use a message passing interface (MPI) to link subcellular network dynamics (parallel thread corresponding to each cell) to cellular fate decisions and phenotypes based on model-specific (user-supplied) rules. Such distributed structure of the models combined with parallel computation will enable unprecedented scalability and mechanistic abstraction. Additionally, ParCell will use a novel load-balancing scheme for arbitrary model scalability in dynamic and heterogeneous cloud environments. The models can be made as mechanistic as any single-cell reaction network without adding model complexity or programming efforts. The PIs will leverage various summer camp programs organized by the Diversity, Outreach, and Womens Programs to recruit female and underrepresented minority students into the project.
Agency: NSF | Branch: Continuing grant | Program: | Phase: EXP PROG TO STIM COMP RES | Award Amount: 400.00K | Year: 2016
Understanding systems of interacting particles is one of the key challenges of physics, and has both fundamental and technological relevance. Such systems generally cannot be fully described in closed analytical form (mathematical expressions that can be written down) for more than two particles, even if their individual properties and the forces between them are precisely known. This dilemma is well-known as the few-body problem and it limits the extent to which one can predict the states of the particles (e.g. their positions and velocities) for any time in the future. Therefore, advancing the knowledge of phenomena that emerge due to the complex interplay of several particles requires the joined theoretical and experimental exploration for a wide range of situations. In this project, few-body phenomena of quantum systems consisting of atoms, their electrically charged components (electrons and ions), and photons (the particles of light) will be studied. Such quantum systems represent an ideal testing ground of few-body physics for multiple reasons: First, few-body effects in these systems are ubiquitous and relevant to many research fields and numerous technical applications, particularly in areas such as materials science, quantum chemistry, biological science, and information processing. Second, advanced experimental techniques are available which allow manipulation of the parameters of the few-particle quantum state with a high degree of control and accuracy. Moreover, modern spectrometers enable snapshots to be taken of the states change over time, allowing details of the states dynamics to be analyzed. Techniques for the control of atomic few-body systems and for the analysis of their dynamics which have been developed in the past twenty years (largely independently of each other) will be combined in the present project for the first time. This will enable the observation of few-body quantum phenomena while being able to tune the system parameters, and it will allow benchmarking theoretical models.
On a more technical level, this project involves the control and analysis of atomic few-body systems using laser cooling and manipulation techniques to prepare a large variety of initial states, ranging from single excited or polarized lithium atoms to large ensembles of atoms that are cooled to quantum-degeneracy. Systems of only very few atoms can be confined in a micrometer-sized optical dipole trap and their interaction can be tuned close to Feshbach resonances. For the analysis, a reaction microscope will be employed allowing the coincident measurements of the momentum vectors of atomic fragments after ionization of the lithium atoms by femtosecond or attosecond laser pulses. In essence, there are three fundamental questions to be addressed in the proposed experiments: First, how do the ionization dynamics depend on the relative orientation (or helicity) of an ionizing laser field and a polarized target atom? Such experiments will help to understand fundamental symmetries and ultimately control the interaction of laser fields with chiral (atomic or molecular) targets, which play a crucial role e.g. in biochemistry. Second, how is the disintegration of an atom due to the interaction with an ionizing field influenced by its environment? This is experimentally only studied for clusters or solid targets, but largely unexplored for more dilute systems. Apart from the fundamental importance of this question, the dependence of the ionization dynamics on the environment is relevant to the understanding of the damage of biological tissue due to radiation. Finally, how does the correlated wave function of a few-particle system change as a function of the particle number and interaction type and strength? The possibility to engineer simple few-body systems and observe such systems comprehensively would allow one to simulate and understand fundamental quantum phenomena that occur in natural or artificial materials.
Agency: NSF | Branch: Standard Grant | Program: | Phase: CDS&E | Award Amount: 403.20K | Year: 2016
Richard Dawes of the Missouri University of Science and Technology is supported by an award from the Chemical Theory, Models and Computational Methods program for his work on methods that create highly accurate potential energy surfaces (PES). This award is cofunded by the Computational and Data-Enabled Science and Engineering program in the Division of Advanced Cyberinfrastructure. The PES are central to how chemists think about the structure and dynamics of molecular systems, in terms of minima and asymptotes connected by paths across landscapes or over energetic barriers. The potential energy surfaces that Dawes and his coworkers create are used to study molecular dynamics and spectroscopy data that is relevant to many chemical processes including atmospheric chemistry, astrochemistry, and combustion. A major thrust of the project is to develop a user-friendly, freely-distributed software package to support and enable members of a wider community, both experimentalists and theorists, to create their own potential energy surfaces to study a large variety of different systems.
The goal of this project is to develop user-friendly software that acts as a bridge between codes that perform electronic structure calculations and those that compute the dynamics of interest. The idea is to represent the electronic structure data with such high-fidelity that the computed dynamics directly reflect the underlying level of electronic structure calculations. (The fit only serves to render affordable the millions of potential evaluations required by dynamics codes). Very high fitting accuracy is required in order to assess the performance of subtly different methods or the importance of small correction terms. Previous work by Professor Dawes has resulted in suitably accurate interpolative methods known as interpolating moving least squares (IMLS). The automated fitting methods that were developed have reduced the bottleneck of PES generation and produced a stockpile of spectroscopically accurate PES. The current plan is to develop a generalized, extendable software distribution that others in the community can use to generate and fit their own PES. The particular focus of this project is on processes involving several coupled PES. This involves systematic studies of systems which include various non-adiabatic interactions between electronic states such as conical intersections, Renner-Teller interactions, and spin-orbit coupling. Accurate treatment of these effects in real applications represents the frontier of theoretical dynamics.
Agency: NSF | Branch: Standard Grant | Program: | Phase: MAJOR RESEARCH INSTRUMENTATION | Award Amount: 897.02K | Year: 2016
This Major Research Instrumentation award will develop a unique additive manufacturing (AM) system to fabricate freeform parts with advanced materials. AM, the process of directly depositing a 3D solid object from a digital model, makes it possible to produce virtually any geometric complexity with very little impact on cost but current AM technologies have not achieved their full potential. This project will develop the research infrastructure to advance AM technologies, with the focus on capacities that differ from conventional manufacturing processes, such as the ability to create materials with properties not generally observed in nature and structures with multiple materials. These capabilities will lead to breakthrough manufacturing technologies, such as producing much stronger and lighter products that cannot be currently made and repairing parts so that they have enhanced strength. Graduate and undergraduate students will be directly involved in the instrumentation design and integration, thereby training the next generation of instrumentalists. AM2 design concepts will be used for team design projects in senior and graduate courses and the instrumentation will be integrated with research projects for the NSF REU Additive Manufacturing site and GAAN Doctoral Research and Training in Direct Digital Manufacturing. The instrumentation will also be available for use by industry and other institutions through the Center for Aerospace Manufacturing Technology.
This project will establish the critical research infrastructure to effectively fabricate novel materials through a high performance deposition system, real-time monitoring and control, and the knowledge required to control the process. Development efforts will focus on advances in the high speed deposition mechanism, substrate liquid cooling design, chamber cooling strategy, elemental material delivery system and sensor integration. The developed system will enable researchers to 1) investigate freeform fabrication of materials that are several times harder and stronger than stainless steels; 2) investigate freeform fabrication of materials that can potentially integrate multiple materials with traditionally incompatible properties into one unified part; and to repair structures to be stronger than their original condition which will revolutionize remanufacturing products; 3) fabricate parts using elemental powders so that real-time material customizability can be achieved; 4) enhance and validate critical multi-scale and multi-physics modeling and analysis for AM processes; 5) develop novel advanced manufacturing applications; and 6) greatly enhance several existing research and education AM projects. The instrumentation will be available to industry through the Center for Aerospace Manufacturing Technology to facilitate collaboration and technology transfer.
Agency: NSF | Branch: Standard Grant | Program: | Phase: CYBER-PHYSICAL SYSTEMS (CPS) | Award Amount: 199.00K | Year: 2017
Ocean Big Data (OBD) is an emerging area of research that benefits ocean environmental monitoring, offshore exploration, disaster prevention, and military surveillance. It is now affordable for oil and gas companies, fishing industry, militaries, and marine researchers to deploy physical undersea sensor systems to obtain strategic advantages. However, these sensing activities are scattered, isolated, and often follow the traditional deploy, wait, retrieve, and post-process routine. Since transmitting information underwater remains difficult and unreliable, these sensors lack a cyber interconnection, which severely limits ocean cyber-physical systems. This project aims to providing a viable cyber interconnection scheme that enables distributed, efficient, ubiquitous, and secure (DEUS) data delivery from underwater sensors to the surface station. The proposed cyber interconnection scheme features cheap underwater sensor nodes with energy harvesting capability, a fleet of autonomous underwater vehicles (AUVs) for information ferrying, advanced magnetic-induction (MI) antenna design using ferrite material, distributed algorithms for efficient data collection via AUVs, and secure data delivery protocols. The success of this project will help push the frontier of Internet of Things in Oceans (IoTO) and OBD, both of which will find numerous underwater applications in offshore oil spill response, fisheries management, storm preparedness, etc., which impact the economy and well-being of not only coastal regions but also inland states. The project will also provide special interdisciplinary training opportunities for both graduate and undergraduate students, particularly women and minority students, through both research work and related courses on underwater wireless communication, network security, and AUV designs.
The DEUS project provides a viable cyber interconnection scheme that enables distributed, efficient, ubiquitous, and secure data delivery in underwater environment via four synergistic thrusts: (1) integration of underwater wireless sensor and communication systems, which will enhance the current MI and light communication means of underwater sensors, integrate acoustic transmission systems for long-range communications between anchor nodes and AUVs, and design energy harvesting and replenishment solutions to prolong the lifetime of underwater sensors (30+ years); (2) distributed and ubiquitous data delivery via multiple AUVs, which aims to collect the distributed data and deliver them ubiquitously throughout the underwater network by employing ferrite material and triaxial induction antennas and mounting them outside of the AUV body for MI enhancement, and developing algorithms of multiple AUVs path-planning, trajectory optimization, etc. under dynamic network conditions; (3) efficiency and security in data delivery, which designs network algorithms to improve the efficiency and security of data delivery. Instead of collecting data from every sensor via acoustic communications, the AUVs choose some sensors to collect data with the high data rate transmission mode in near field (e.g., light), and allowing the sensor far away from the AUVs to send its data either directly to AUVs via acoustic wave or to its nearby chosen sensors via MI/light communications. A secure data delivery scheme will also be developed to not only secure the data delivery against typical malicious attacks and guarantee the integrity of collected data, but also allow the data aggregation of one business entity without knowing others private business information; (4) experimental validation and testing, which will verify the proposed data delivery schemes, and quantitatively present the performance gains through simulations, experiments and field test, based on existing facilities.
Agency: NSF | Branch: Standard Grant | Program: | Phase: ELECT, PHOTONICS, & MAG DEVICE | Award Amount: 500.01K | Year: 2017
Abstract Title: CAREER: Flat Singular Optics: Generation and Detection of Optical Vortex Beams with Plasmonic Metasurfaces in Linear and Nonlinear Regimes
The study of the phenomena of phase and polarization singularities is known as singular optics. Vortex beams display singular scalar features with phase singularities in the beam center, while polarization singular beams present singular vectorial features with one undefined polarization parameter. The current open research challenges in singular optics are how to generate pure vortices with broad bandwidth and polarization singular beams on chip, how to realize compact linear and nonlinear vortex beam converters, and what kind of device enables direct identification of vortex topological charges. In order to address these challenges, in this research, uniquely designed plasmonic metasurfaces made of ultrathin metallic nanostructures are utilized as a powerful and compact platform for exploring flat singular optics, generating singular optical beams in both linear and nonlinear regimes, as well as detecting vortex beam orbital angular momentum with optoelectronic integration. This research paves the way for advancing many applications in optical communication, beam shaping and conversion, optoelectronic devices, and optical sensing and imaging. The research of singular optics and metasurfaces is integrated with education and outreach activities in order to enhance various levels of education, including undergraduate and graduate students training, underrepresented and female students recruitment, women in optics student group organization, interdisciplinary metamaterials course development and outreach activities for K-12 students.
The goals of the research are to explore the generation and detection of optical vortex beams using ultra-thin plasmonic metasurfaces in both linear and nonlinear regimes in order to solve open research challenges and create new opportunities in singular optics, gain fundamental knowledge of spin-orbit and orbit-orbit interactions and optical angular momentum conservation. The research uses the approaches of linear and nonlinear optical design and simulation, plasmonic metasurface sample nanofabrication, optical and optoelectronic device characterizations to study the underlying physics of metasurface based light manipulation and light-matter interaction in singular optics, nonlinear optics, and spin-orbit photonics. The intellectual significance of the activity includes: (i) generation of pure optical vortex beams and polarization singular beams to advance the research of complex structured light manipulation and topological singularities; (ii) exploration of second- and third-harmonic vortex beam generation and beam shaping in nonlinear plasmonic metasurfaces to reveal the conservation law of orbital angular momentum and create compact nonlinear beam converters and active photonic devices; (iii) realization of optoelectronic vortex beam detection based on spin and orbital Hall effects in plasmonic metasurfaces to solve the challenge of on-chip orbital angular momentum detection and build functional metasurface based optoelectronic devices.
Agency: National Science Foundation | Branch: | Program: STTR | Phase: Phase I | Award Amount: 225.00K | Year: 2017
The broader impact/commercial potential of this Small Business Technology Transfer (STTR) Phase I project is to improve and automate cognitive health screening mechanisms used in hospital facilities, by designing and validating a smart chair and a smart wearable based health data-centric novel solution. U.S. population has more than 16 million people (this is rapidly growing further) living with cognitive impairment. There is alarming fact that people with cognitive impairment report more than three times as many hospital stays as individuals with other health conditions. These are driving development of improved cognitive health assessment solutions, that will detect subtle signs of cognitive decline early in daily life. Motivated by these markets in demand, this project is designed to develop a system for advanced and remote screening and monitoring of cognitive health, and also enabling gamified user interaction for cognitive rehabilitation. The developed technology has large potential to help elderly people prone to levels of dementia (from mild cognitive impairment to Alzheimer's disease), and slowly rehabilitating patients dealing with cognitive decline (stroke survivors and cancer patients under treatment). The designed platform and cognitive scoring algorithms developed in this project will be tested and validated with real patient study at hospital facility. The proposed project is a research, development and clinical studies effort to design and validate an advanced and remote screening, monitoring and rehabilitation solution for cognitive health. Such cognitive health management system will be achieved by technological innovation in multi-modal sensing and machine learning based behavioral data analytics from a smart chair and a custom wearable device (used separately), along with cognitive psychology driven gamified interactions and interventions. The smart chair system will capture patient non-verbal response and stress patterns, when answering to cognitive questionnaire. The smart wearable system (used with a few custom Bluetooth beacons in proximity) will analyze normalcy, forgetfulness, confusion in patient, while performing daily life cognitive tasks (such as baking a cake or cookie) in home-like kitchen facility in hospital. The main research objective includes two novel cognitive health scoring mechanism from user response with chair based cognitive questionnaire, and wearable based cognitive task performance quality. It will also design user interactions and interventions for cognitive rehabilitation and behavior reinforcement. This is a very impactful project with team's direct ongoing collaboration with hospital and neurologist, and access to large number of patients with range of cognitive disabilities (in accordance with IRB and HIPAA compliance).
Agency: NSF | Branch: Standard Grant | Program: | Phase: ENVIRONMENTAL ENGINEERING | Award Amount: 332.88K | Year: 2016
Increased production and use of man-made chemicals have benefited development, and, human welfare greatly, including unprecedented food production and increasing world-wide standard of living. However the benefit gained has the unintended consequence of increasing ubiquity and diversity of emerging compounds in our biosphere. Society is shortening the water cycle via water reuse and reclamation while advancements in organic molecule design, such as advanced pharmaceuticals and industrial compounds, introduce new potential contaminants at an ever-increasing pace. This proposal describes a high throughput screening, tiered approach to efficiently assess plant uptake and translocation of emerging and fugitive compounds. Understanding the uptake and distribution from a physicochemical perspective will advance knowledge of emerging and fugitive compound fate in plant systems.
To achieve the goal of high throughput screening, in silico (using computers) tools will be developed to predict plant uptake from physicochemical properties. In silico predictions will be validated using agronomic plants subjected to specific emerging and fugitive compounds spanning a broad chemical space. To advance knowledge of transport through the plant vascular systems after uptake, poly-parameter linear free energy relationships will be developed to predict fate of the diverse emerging and fugitive compounds in plant tissues (e.g., lignin). From individual poly-parameter linear free energy relationships, a composite partitioning model will be developed to better elucidate distribution within vascular plants, and to offer insight to emerging and fugitive compound fate in food compartments such as edible stalks, fruits and grains. Fundamental knowledge will advance in several ways. First, single-parameter prediction of emerging and fugitive compound uptake are inaccurate for polar compounds, as chemical space covers multiple dimension. 5-dimensional partitioning poly-parameter linear free energy relationships will be parameterized using high quality data. Second, the in silico predictive tools integrate fundamental chemical and biological understanding, beyond single-parameter relationships or generic box models. Third, a standardized approach to predict and measure emerging and fugitive compounds translocation by plants will increase the value of future research. The tiered approach combines multidisciplinary knowledge to generate a holistic picture of chemical transport and fate in vascular plants, particularly relating to food. Increasing knowledge on organic molecule uptake and translocation in plants will widely impact science and engineering disciplines, including: a) predicting crop uptake of pollutants from irrigation waters, b) though phytoremediation to remove contaminants from the subsurface, and, c) guiding the use of plants as biosensors of subsurface contamination in phyto-forensics. All three approaches protect human health. Agrochemical development and fundamental plant biology will also benefit from the advanced tools to understand and predict organic molecule transport, including plant hormones and community signaling. Given recent breakthroughs that elucidate similarities in transmembrane transport in roots to mammalian intestinal membranes and the blood-brain barrier, the long term merit could be tremendous to many fields. The proposed platform forecasts transport of proposed compounds, many destined to be future emerging and fugitive compounds. Broadly stated, knowledge resulting from this endeavor will address critical societal and health issues, many related to water-food interactions. Society needs to be more proactive to protect human health as human interactions shorten the water cycle, potentially funneling contaminants into and potentially up the food chain. The findings will be incorporated into numerous outreach efforts with a variety of hands-on demos and media-based examples that appeal to multiple levels of education and a variety of citizenry. All efforts rely on familiarity of plants to convey larger messages of health and pollutant impacts. This work directly translates to education platforms and can benefit both contamination assessments and safety of urban gardening in blighted urban areas, where increased exposure potential exists. Overall, society needs better knowledge on pollutant entry to global food supplies to avoid instances where widespread chemical use results in global distribution decades before a pollutant is noted as emerging.
Agency: NSF | Branch: Standard Grant | Program: | Phase: COMMS, CIRCUITS & SENS SYS | Award Amount: 362.51K | Year: 2016
Active Microwave Thermography (AMT) is based on the integration of microwave and thermographic nondestructive testing (NDT), which utilizes a microwave heat excitation for subsequent thermal measurements. AMT has strong potential for controlled, rapid, and effective inspection of targeted (surface or volumetric) regions of rehabilitated concrete infrastructure. By combining the benefits of multiple NDT techniques, unique features of multiple methods can be brought together to achieve new results that one method alone cannot achieve. A significant advantage to the integration of these techniques lies in the ability to capitalize on microwave signal properties to achieve focused and localized heating. The outcomes of this research will result in a new NDT method with broad-reaching applications for inspection of aging infrastructure, which has direct benefits to society through cost-savings and general safety of the public. In addition, this research establishes an approach for simulation and experimental aspects that will serve as a solid developmental platform for expansion of AMT to other fields such as medical, aerospace, and security. Research opportunities within this project will be offered to underrepresented (minority/female high school through graduate) students. Further, this work will have a significant impact on the content of several undergraduate and graduate courses taught in the electrical and computer engineering department, the civil, architectural, and environmental engineering department, and the chemistry department at Missouri University of Science and Technology.
This work aims to develop the general science behind a unique nondestructive testing (NDT) method for controlled, rapid, and effective inspection of targeted (volumetric) regions of a structure through the integration of microwave NDT and thermography, referred to as Active Microwave Thermography (AMT). A significant advantage to merging these techniques lies in the ability to capitalize on the strengths of microwave NDT and thermography while improving their limitations. A key benefit to utilizing microwave energy as a heat source is the potential for controlled and targeted heating, thereby improving the efficacy of thermography. The tasks proposed in this project will be conducted through multi-physics based microwave and thermal simulations and will be subsequently verified with measurements. A materials characterization effort will be conducted to characterize the materials used in rehabilitated concrete structures from an electromagnetics point-of-view. This information will be invaluable for other applications where such materials are utilized. Moreover, as part of the material characterization effort, this work capitalizes on microwave-induced heating to determine information about electric dipole moments. This study will also provide information necessary to build predictive models for how a material will physically respond to microwave heating and eventually predetermine thermal signatures for healthy and defective samples. Further, these tasks will address critical issues including the effectiveness and optimization of microwave heating and the detection sensitivity of AMT to flaws/defects. Moreover, current literature states that modulated lock-in methods are not possible for AMT due to the interaction between the microwave energy and thermal camera hardware. However, AMT systems can be designed to reduce the electromagnetic interference with the thermal camera, thereby alleviating this limitation and opening the door for a more successful utilization of this technique. Additionally, this study will explore the potential for advanced signal processing techniques that utilize microwave-specific attributes such as polarization control and patterned heating that will lead to improved defect detection and characterization capabilities. Overall, the project outcomes will not only result in a new inspection method for the transportation and infrastructure industries, but also a development platform for applications of AMT in other important industries such as medical, aerospace, and security.